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Modeling MESFETs and HEMTs intermodulation distortion behavior using a generalized radial basis function network

机译:使用广义径向基函数网络对MESFET和HEMT互调失真行为进行建模

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This paper proposes a generalized radial basis function (GRBF) network to accurately describe drain to source current nonlinearity for intermodulation distortion (IMD) prediction of MESFETs and HEMTs applications in their saturated region. Trying to analytically reproduce the nonlinearities second and third order Taylor-series coefficients, responsible for IMD performance in these devices, may result in a quite difficult task. Neural networks were introduced as a robust alternative for microwave modeling, mostly employing the black-box model type approach of the multilayer perceptron network. The GRBF network we consider is a generalization of the RBF network, which takes advantage of problem dependent information. Allowing different variances for each dimension of input space, the GRBF network makes use of soft nonlinear dependence of the drain to source current derivatives with drain to source voltage for improving accuracy at reduced cost. The network structure and its learning algorithm are presented. Results of its performance are compared to other structures with similar amounts of parameters. Carrier to intermodulation (C/I) predictions validate this approach for precise IMD control versus bias and load in class A amplifiers applications.©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 261–276, 1999.
机译:本文提出了一种广义径向基函数网络(GRBF),以准确描述漏极到源极电流的非线性,以预测MESFET和HEMT在其饱和区域中的互调失真(IMD)。试图分析性地再现非线性和二阶泰勒级数系数,这些系数负责这些设备中的IMD性能,可能会导致一项艰巨的任务。引入神经网络作为微波建模的可靠替代方法,其中大多数采用了多层感知器网络的黑盒模型类型方法。我们考虑的GRBF网络是RBF网络的概括,它利用了与问题相关的信息。 GRBF网络允许对输入空间的每个维度使用不同的方差,因此利用了漏极到源极电流导数与漏极到源极电压的软非线性相关性,从而以降低的成本提高了精度。介绍了网络结构及其学习算法。将其性能结果与具有类似参数量的其他结构进行比较。载波互调(C / I)预测验证了这种方法可在A类放大器应用中实现精确的IMD控制,偏置和负载。©1999 John Wiley&Sons,Inc.国际J RF和微波CAE 9:261–276,1999。

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